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Parsing Videos of Actions with Segmental Grammars
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Semi-Markov Models and Applications
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Event Modeling and Recognition Using Markov Logic Networks
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Modeling Temporal Structure of Decomposable Motion Segments for Activity Classification
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Cutting-plane training of structural SVMs
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Semantic Representation and Recognition of Continued and Recursive Human Activities
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Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models
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Parametric hidden Markov models for gesture recognition
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Recognition of visual activities and interactions by stochastic parsing
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Space-Time Behavior Based Correlation
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Hidden Conditional Random Fields for Gesture Recognition
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Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition
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Joint segmentation and classification of human actions in video
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Probabilistic event logic for interval-based event recognition
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Learning latent temporal structure for complex event detection
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Detecting activities of daily living in first-person camera views
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Attribute Grammar-Based Event Recognition and Anomaly Detection
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Recognizing action at a distance
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Event Detection in Crowded Videos
inferredcites →https://doi.org/10.1109/iccv.2007.4409105openalex-snapshot
Retrieving actions in movies
inferredcites →https://doi.org/10.1109/iccv.2011.6126223openalex-snapshot
Unsupervised learning of event AND-OR grammar and semantics from video
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Three models for the description of language
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Space-Time Behavior-Based Correlation-OR-How to Tell If Two Underlying Motion Fields Are Similar Without Computing Them?
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On-Line Video Event Detection by Constraint Flow
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Learning structural SVMs with latent variables
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Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
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Evaluation of local spatio-temporal features for action recognition
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Recognizing multitasked activities from video using stochastic context-free grammar
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation
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A Diagram Is Worth A Dozen Images
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Unsupervised Semantic Action Discovery from Video Collections
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Hand Action Detection from Ego-centric Depth Sequences with Error-correcting Hough Transform
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Weakly supervised learning of actions from transcripts
inferred← citeshttps://doi.org/10.48550/arxiv.1611.05267openalex-snapshot
Temporal Convolutional Networks for Action Segmentation and Detection
inferred← citeshttps://doi.org/10.48550/arxiv.1612.06371openalex-snapshot
Asynchronous Temporal Fields for Action Recognition
inferred← citeshttps://doi.org/10.48550/arxiv.1704.02895openalex-snapshot
ActionVLAD: Learning spatio-temporal aggregation for action classification
inferred← citeshttps://doi.org/10.48550/arxiv.1704.06228openalex-snapshot
Temporal Action Detection with Structured Segment Networks
inferred← citeshttps://doi.org/10.48550/arxiv.1707.06436openalex-snapshot
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
inferred← citeshttps://doi.org/10.48550/arxiv.1712.07294openalex-snapshot
Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning
inferred← citeshttps://doi.org/10.48550/arxiv.1803.05790openalex-snapshot
Temporal Human Action Segmentation via Dynamic Clustering
inferred← citeshttps://doi.org/10.48550/arxiv.1806.11008openalex-snapshot
Modeling Spatio-Temporal Human Track Structure for Action Localization
inferred← citeshttps://doi.org/10.48550/arxiv.1902.00505openalex-snapshot
Differentiable Grammars for Videos
inferred← citeshttps://doi.org/10.48550/arxiv.2006.04843openalex-snapshot
Modeling Long-horizon Tasks as Sequential Interaction Landscapes
inferred← citeshttps://doi.org/10.48550/arxiv.2110.08568openalex-snapshot
ASFormer: Transformer for Action Segmentation
inferred← citeshttps://doi.org/10.70675/12999662zc116z4dcazb558zc835f99061eeopenalex-snapshot
Modélisation structurée et reconnaissance des actions humaines dans les vidéos
inferred← citeshttps://doi.org/10.7298/x4w66hq9openalex-snapshot
Unsupervised Structured Learning Of Human Activities For Robot Perception
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